code: def test_deepvar(
      distr_output,
      num_batches_per_epoch,
      Estimator,
      hybridize,
      use_marginal_transformation,
      use_feat_dynamic_real,
  ):
      estimator = Estimator(
          num_cells=20,
          num_layers=1,
          pick_incomplete=True,
          target_dim=target_dim,
          prediction_length=metadata.prediction_length,
          # target_dim=target_dim,
          freq=metadata.freq,
          distr_output=distr_output,
          scaling=False,
          use_marginal_transformation=use_marginal_transformation,
          use_feat_dynamic_real=use_feat_dynamic_real,
          trainer=Trainer(
              epochs=1,
              learning_rate=1e-10,
              num_batches_per_epoch=num_batches_per_epoch,
              hybridize=hybridize,
          ),
      )
  
      predictor = estimator.train(training_data=dataset.train)
  
      agg_metrics, _ = backtest_metrics(
          test_dataset=dataset.test,
          predictor=predictor,
          evaluator=MultivariateEvaluator(
              quantiles=(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)
          ),
      )
  
      assert agg_metrics["ND"] < 1.5
  
file_path: gluonts/test/mx/model/deepvar/test_deepvar.py
imports: import pytest
  from gluonts.dataset.artificial import constant_dataset
  from gluonts.dataset.common import TrainDatasets
  from gluonts.dataset.multivariate_grouper import MultivariateGrouper
  from gluonts.evaluation import backtest_metrics, MultivariateEvaluator
  from gluonts.mx import DeepVAREstimator
  from gluonts.mx.distribution import LowrankMultivariateGaussianOutput, MultivariateGaussianOutput
  from gluonts.mx.trainer import Trainer
label_desc: Use image node_id 2 for calling a global function with example usage: test_deepvar(distr_output, num_batches_per_epoch, Estimator, hybridize, use_marginal_transformation, use_feat_dynamic_real) without return types